






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort



class 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[Any]				= 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     str=0      )      ->							Any:
           __UpperCAmelCase     				=floats_tensor((1, 3, 128, 128) ,     rng=random.Random(__SCREAMING_SNAKE_CASE      )      )
           __UpperCAmelCase     				=np.random.RandomState(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				={
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """strength""": 0.75,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs



    def _a							(				self      :     Optional[Any]      )      ->							int:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]      )
           assert np.abs(image_slice - expected_slice      ).max() < 1e-1



    def _a							(				self      :     Union[str, Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           __UpperCAmelCase     				=PNDMScheduler.from_config(pipe.scheduler.config ,     skip_prk_steps=__SCREAMING_SNAKE_CASE      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1



    def _a							(				self      :     Optional[Any]      )      ->							Dict:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           __UpperCAmelCase     				=LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           # warmup pass to apply optimizations
           __UpperCAmelCase     				=pipe(**self.get_dummy_inputs()      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1



    def _a							(				self      :     List[Any]      )      ->							List[str]:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           __UpperCAmelCase     				=EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1



    def _a							(				self      :     Union[str, Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           __UpperCAmelCase     				=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1



    def _a							(				self      :     Union[str, Any]      )      ->							Dict:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           __UpperCAmelCase     				=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1




@nightly
@require_onnxruntime
@require_torch_gpu
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @property
    def _a							(				self      :     List[str]      )      ->							Optional[int]:
           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )



    @property
    def _a							(				self      :     Dict      )      ->							int:
           __UpperCAmelCase     				=ort.SessionOptions()
           __UpperCAmelCase     				=False
           return options



    def _a							(				self      :     Dict      )      ->							Any:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           __UpperCAmelCase     				=init_image.resize((768, 512)      )
           # using the PNDM scheduler by default
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
               """CompVis/stable-diffusion-v1-4""" ,     revision="""onnx""" ,     safety_checker=__SCREAMING_SNAKE_CASE ,     feature_extractor=__SCREAMING_SNAKE_CASE ,     provider=self.gpu_provider ,     sess_options=self.gpu_options ,     )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				="""A fantasy landscape, trending on artstation"""

           __UpperCAmelCase     				=np.random.RandomState(0      )
           __UpperCAmelCase     				=pipe(
               prompt=__SCREAMING_SNAKE_CASE ,     image=__SCREAMING_SNAKE_CASE ,     strength=0.75 ,     guidance_scale=7.5 ,     num_inference_steps=10 ,     generator=__SCREAMING_SNAKE_CASE ,     output_type="""np""" ,     )
           __UpperCAmelCase     				=output.images
           __UpperCAmelCase     				=images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 768, 3)
           __UpperCAmelCase     				=np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2



    def _a							(				self      :     List[str]      )      ->							str:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           __UpperCAmelCase     				=init_image.resize((768, 512)      )
           __UpperCAmelCase     				=LMSDiscreteScheduler.from_pretrained(
               """runwayml/stable-diffusion-v1-5""" ,     subfolder="""scheduler""" ,     revision="""onnx"""      )
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
               """runwayml/stable-diffusion-v1-5""" ,     revision="""onnx""" ,     scheduler=__SCREAMING_SNAKE_CASE ,     safety_checker=__SCREAMING_SNAKE_CASE ,     feature_extractor=__SCREAMING_SNAKE_CASE ,     provider=self.gpu_provider ,     sess_options=self.gpu_options ,     )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				="""A fantasy landscape, trending on artstation"""

           __UpperCAmelCase     				=np.random.RandomState(0      )
           __UpperCAmelCase     				=pipe(
               prompt=__SCREAMING_SNAKE_CASE ,     image=__SCREAMING_SNAKE_CASE ,     strength=0.75 ,     guidance_scale=7.5 ,     num_inference_steps=20 ,     generator=__SCREAMING_SNAKE_CASE ,     output_type="""np""" ,     )
           __UpperCAmelCase     				=output.images
           __UpperCAmelCase     				=images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 768, 3)
           __UpperCAmelCase     				=np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2





"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   SCREAMING_SNAKE_CASE__:List[Any]									=			"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )



def 	_lowerCamelCase(	a   ,    a   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    ):
       if attention_mask is None:
              __a       =    np.where(input_ids != config.pad_token_id   ,    1   ,    0				)
       if decoder_attention_mask is None:
              __a       =    np.where(decoder_input_ids != config.pad_token_id   ,    1   ,    0				)
       if head_mask is None:
              __a       =    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=99    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=4    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		lowerCamelCase=0.02    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    eos_token_id
        __a       =    pad_token_id
        __a       =    bos_token_id
        __a       =    initializer_range


 def 						a__   ( self						):
        __a       =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size						)    ,		3    ,		self.vocab_size						)
        __a       =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,		dtype=np.intaa						))    ,		-1						)

        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)

        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		initializer_range=self.initializer_range    ,		use_cache=lowerCamelCase    ,		)
        __a       =    prepare_blenderbot_inputs_dict(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        return config, inputs_dict


 def 						a__   ( self						):
        __a ,   __a       =    self.prepare_config_and_inputs()
        return config, inputs_dict


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype="i4"						)

        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
            ]    ,		axis=-1    ,		)

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)

        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)
        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






@require_flax
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Dict							  =						99


 def 						a__   ( self						):
        __a       =    np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]    ,		dtype=np.intaa    ,		)

        __a       =    input_ids.shape[0]
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=24    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=32    ,		decoder_ffn_dim=32    ,		max_position_embeddings=48    ,		eos_token_id=2    ,		pad_token_id=1    ,		bos_token_id=0    ,		)
        return config, input_ids, batch_size


 def 						a__   ( self						):
        __a ,   __a ,   __a       =    self._get_config_and_data()
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    lm_model(input_ids=lowerCamelCase						)
        __a       =    (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=14    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=8    ,		decoder_ffn_dim=8    ,		max_position_embeddings=48    ,		)
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,		dtype=np.intaa						)
        __a       =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,		dtype=np.intaa						)
        __a       =    lm_model(input_ids=lowerCamelCase    ,		decoder_input_ids=lowerCamelCase						)
        __a       =    (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,		dtype=np.intaa						)
        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        self.assertEqual(shifted.shape    ,		input_ids.shape						)
        self.assertEqual(lowerCamelCase    ,		n_pad_before - 1						)
        self.assertTrue(np.equal(shifted[:, 0]    ,		2						).all()						)






@require_flax
class  snake_case__      (       snake_case_,						unittest.TestCase,						snake_case_       ):
 _snake_case       :					Any							  =						True
 _snake_case       :					int							  =						(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _snake_case       :					str							  =						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


 def 						a__   ( self						):
        __a       =    FlaxBlenderbotSmallModelTester(self						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
                      __a       =    model_class(lowerCamelCase						)

                      @jax.jit
                      def encode_jitted(lowerCamelCase    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
                             return model.encode(input_ids=lowerCamelCase    ,		attention_mask=lowerCamelCase						)

                      with self.subTest("JIT Enabled"						):
                             __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    model_class(lowerCamelCase						)
                      __a       =    model.encode(inputs_dict["input_ids"]    ,		inputs_dict["attention_mask"]						)

                      __a       =    {
                          "decoder_input_ids": inputs_dict["decoder_input_ids"],
                          "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                          "encoder_outputs": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
                             return model.decode(
                                 decoder_input_ids=lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		encoder_outputs=lowerCamelCase    ,		)

                      with self.subTest("JIT Enabled"						):
                             __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)






 @slow
 def 						a__   ( self						):
        for model_class_name in self.all_model_classes:
               __a       =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"						)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __a       =    np.ones((1, 1)						) * model.config.eos_token_id
               __a       =    model(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)
